code: def get_exported_module(tsdata, forecaster_path, drop_dt_col):
      from bigdl.chronos.data.utils.export_torchscript import (
          get_processing_module_instance,
          get_index,
      )
  
      if drop_dt_col:
          tsdata.df.drop(columns=tsdata.dt_col, inplace=True)
  
      id_index, target_feature_index = get_index(
          tsdata.df,
          tsdata.id_col,
          tsdata.target_col,
          tsdata.feature_col,
      )
  
      preprocess = get_processing_module_instance(
          tsdata.scaler,
          tsdata.lookback,
          id_index,
          target_feature_index,
          tsdata.scaler_index,
          "preprocessing",
      )
  
      postprocess = get_processing_module_instance(
          tsdata.scaler,
          tsdata.lookback,
          id_index,
          target_feature_index,
          tsdata.scaler_index,
          "postprocessing",
      )
  
      inference = torch.jit.load(forecaster_path)
  
      return torch.jit.script(
          ExportForecastingPipeline(preprocess, inference, postprocess)
      )
  
imports: import torch
  import torch.nn
  import warnings
  import random
  import numpy
  from torch.utils.data import TensorDataset, DataLoader
  import numpy
  import multiprocessing
  import warnings
  import math
label_desc: Use image node_id 15 for calling a global function with example usage: get_exported_module(tsdata, forecaster_path, drop_dt_col) and returns: torch